2D Lane Detection.
In recent years, there have been impressive developments in the area of 2D lane detection using deep neural networks, thanks to the effectiveness of CNN models. In the 2D lane detection, it is divided into three directions, pixel-wise segmentation, row-wise segmentation, and curve parameters. [1]}, [2]} consider 2D lane detection as a multi-category segmentation task based on pixel-wise, these methods set the limit number of lane lines, and the computing cost is expensive. 2D lane detection is also regarded as two-class segmentation by [3]}, [4]}, and then combines with the embedding way to cluster each lane line to achieve the variable number of lane detection. [5]}, [6]}, [7]} focus on the row-wise level to detect the 2D lane lines. By setting the row-anchor in the row direction and setting the grid cell in the colume direction to model the 2D lane lines on the image space, row-wise methods greatly improve the speed of inference. [8]}, [9]} argue that the lane line can be fitted by specific curve parameters on the 2D image space. So it is proposed that the 2D lane detection can be converted into the problem of curve parameters regressing by detecting starting point, ending point and curve parameters. However, these methods based on 2D image need to combine camera intrinsic and extrinsic parameters for IPM projecting to the ground in post-processing, which are based on the ideal situation of the ground plane hypothesis. As mentioned in Chapter 1, in the actual driving process, 2D lane detection + IPM is difficult to get the exact position of the real lane line.
